DETERMINING ROBUST PARAMETERS IN STABILIZING SET OF BACKSTEPPING BASED NONLINEAR CONTROLLER FOR SHIP COURSE KEEPING

The authors have addressed an important topic that is needed for backstepping algorithm to guarantee the robust performance of the closed-loop system. A novel method of determining parameters was presented based on ship maneuvering empirical knowledge and closed-loop shaping theory, and theoretical proof had shown the uniformly asymptotic stability of an established nonlinear Nomoto model. However, the following important points are suggested for the improvement of this paper.

Estimating Critical Latency Affecting Ship’s Collision in Re-mote Maneuvering of Autonomous Ships

Estimation of the critical latency that can cause collision in remote maneuvering of autonomous ships can provide a clue to avoid collisions. The concept of estimating the critical latency was established using the turning circle formed by the turning maneuver of the own ship, and critical latency was estimated using the radius of the turning circle with the turning time ratio. The turning circle was observed using the turning trajectory of the give-way vessel measured in the ship maneuvering simulation experiment. Experimental results demonstrated that the proposed method is capable of identifying both the location and time of the collision due to critical latency. As a result, a clue to avoid possible collision in remote maneuvering caused by critical latency was deduced.

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

The accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. The ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. This research finding can help a ship’s crew to design more reasonable navigation routes in polar channels.

Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays

The paper presents the results of a study on improving the safety of navigation, during maneuvers in restricted areas, with the use of an augmented virtuality navigation information display. The augmented virtuality solution has been developed by one of the authors and has been described in a previous paper. A set of simulation scenarios has been proposed to study various maneuvers with different types of ships. Models of existing areas and existing ships have been used so the study can be recreated and reevaluated with different types of interfaces. The study is focused on comparing safe maneuvering areas with different information sources used. The results showed that augmented virtuality solution can indeed decrease the safe maneuvering areas and thus increase the safety of navigation for maneuvers in restricted areas.

Identification and Prediction of Ship Maneuvering Motion Based on a Gaussian Process with Uncertainty Propagation

Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling techniques to identify ship maneuvering systems has attracted considerable attention. The Gaussian process has high precision and strong generalization ability in fitting nonlinear functions and requires less training data, which is suitable for ship dynamic model identification. Compared with other machine learning methods, the most obvious advantage of the Gaussian process is that it can provide the uncertainty of prediction. However, most studies on ship modeling and prediction do not consider the uncertainty propagation in Gaussian processes. In this paper, a moment-matching-based approach is applied to address the problem. The proposed identification scheme for ship maneuvering systems is verified by container ship simulation data and experimental data from the Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN) database. The results indicate that the identified model is accurate and shows good generalization performance. The uncertainty of ship motion prediction is well considered based on the uncertainty propagation technology.